Tag Archives: Privacy

25 Years of Mass Surveillance Is Enough

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/25-years-of-mass-surveillance-is-enough.html

This essay was written with Cindy Cohn, and originally appeared in Lawfare.

One of the many legacies of the terrorist attacks of Sept. 11 is the government-wide shift from targeted surveillance—such as individual wiretaps or pen register/trap and trace orders—to mass surveillance techniques—such as tapping into the internet backbone or mass collection of telephone or internet metadata. The legal and technical architecture of modern mass surveillance, initially framed as a necessary defense against terrorist threats, has grown far beyond that justification and national security in general. Mass surveillance is now a routine tool used by law enforcement. ICE uses it in immigration actions and against people exercising their First Amendment rights to protest. It’s also increasingly part of private security systems, such as facial recognition at venues such as Madison Square Garden and networked Flock license plate capture systems on roads and in parking lots.

The interrelation between private and governmental mass surveillance is worth examining. Surveillance is the business model of the internet; companies like Google and Facebook constantly spy on their users’ behavior. From the National Security Agency relying on data collected by telecommunication and internet companies, to local sheriffs and ICE agents relying on cellphone location data and privately managed automatic license plate readers, governments primarily obtain the mass surveillance information through private companies. Increasingly, access doesn’t just come through legal processes, either. FBI Director Kash Patel recently confirmed in congressional testimony that the agency is purchasing information on Americans from data brokers and intends to continue to do so.

This pipeline from private collection to governmental collection means that as companies collect more information for surveillance capitalism purposes, more is available to law enforcement as well. And as the technology for mass surveillance and analysis improves, especially with the increased use of AI technologies, the problems attendant to mass surveillance grow as well.

After 9/11, the idea that the government could surveil the population to safety took hold. In 2001, the fear of terrorism reached a frequency and intensity never before seen. Along with that came the fear that the enemy could be anyone, anywhere. As a result, the government’s response was to watch everyone, everywhere. This line of reasoning underpinned the shift from targeted to mass surveillance. Or, in the words of an internal National Security Agency (NSA) presentation that was made public as part of Edward Snowden’s 2013 disclosures, a government that can “Collect it All,” “Process it All,” “Exploit it All,” “Partner it All,” and “Sniff it All,” will ultimately, “Know it All.” Similar rationales support the rise of domestic mass surveillance: if law enforcement could see and hear everything, it could more effectively interdict and solve serious crimes.

The national security community has never provided a full analysis of the costs and benefits of these mass surveillance programs, either in terms of taxpayer dollars or diversion of resources from other efforts—or any demonstration that those techniques stopped attacks that otherwise they would not have been able to prevent. While the NSA occasionally presents examples of the successes due to its mass surveillance programs, especially when those techniques are under public pressure, the examples also regularly fall apart upon serious scrutiny. And even if some utility exists, it must be seriously weighed against the costs.

Similarly, there has never been any comprehensive analysis about whether domestic immigration or law enforcement’s use of these techniques actually makes people safer, or whether other techniques could produce the same results. Instead, both the police and the companies selling these tools float anecdotes and dubious data. For example, Flock’s data equates the number of law enforcement hits in their database with actually solving crimes.

Twenty-five years after 9/11, it seems reasonable to step back and evaluate the costs of this shift to mass surveillance, especially in terms of Americans’ rights and freedoms.

The Shift

The easiest place to see a shift to mass surveillance was in the government’s decision immediately after 9/11 to collect Americans’ telephone records. The program started under an argument of pure executive power as the “President’s Surveillance Program.” But in 2006, that argument secretly shifted to a novel interpretation of Section 215 of the Patriot. Act which had only previously authorized more targeted access to record. While some media and public interest organizations struggled to force the government to reveal the program as early as late 2005, the government only officially confirmed it after the 2013 Snowden disclosures. In 2015, the Second Circuit Court of Appeals rejected the government’s interpretation of Section 215 as allowing mass collection of telephone records. Later the same year, Congress passed the USA Freedom Act. While this new law still allows collection of a tremendous amount of domestic telephone records, it ended the indiscriminate mass collection that had occurred for nearly fourteen years.

Other shifts to mass surveillance continue through today. The NSA launched its Upstream program, which involved intercepting both metadata and content from key telecommunications junctures inside the U.S., soon after 9/11. It was also initially conducted under a claim of purely presidential authority. This program was brought under marginal congressional and programmatic (not targeted) Foreign Intelligence Surveillance Act (FISA) court review via Section 702 of the 2008 FISA Amendments Act. In 2017, more than15 years after its inception, the NSA ended content searches due to FISA court pressure, but the mass collection continues.

Despite the stated goal of conducting mass spying only on people outside the U.S.—which itself is problematic given international law’s requirement that surveillance be both necessary and proportionate—mass surveillance collects a tremendous amount of U.S. persons’ communications. This can happen because people communicate with people abroad, or because of overcollection—when government agencies gather far more personal data on non-targeted US persons than authorized by law. The concerns about collecting Americans’ data on U.S. soil led Congress to allow the program to officially expire in 2026, although the previously-approved mass surveillance itself continues until at least Spring of 2027.

The shift to mass surveillance would be notable enough even if it remained only a strategy of the intelligence community. It has not. Americans are awash in mass surveillance. Networks of automated license plate readers such as those offered by Flock and Vigilant Solutions blanket both public and private roadways and parking lots. These networks often allow searches by law enforcement, including across jurisdictions. They are, for example, being used to track people seeking abortions across state lines. Facial recognition tools, once the province of only the more elite parts of federal law enforcement, are increasingly used by Immigration and Customs Enforcement agents on immigrants and protesters, in airports by the Transportation Security Administration, as well as by private entities. And, of course, modern phones track users’ locations constantly—and that information is readily available to law enforcement, often with only minimal process protections.

Constitutional Costs

Regardless of the murkiness of its actual usefulness, the shift from targeted to mass surveillance has profound implications for Americans’rights. It has created risks that have become increasingly evident, especially under the Trump administration.

At a basic level, the Fourth Amendment guarantees that citizens can be secure in their “persons, houses, papers and effects” from unreasonable searches. Warrants breaching that security should be supported by probable cause and particular descriptions of the place to be searched and items to be seized. Mass surveillance turns that promise on its head, allowing access to our “papers and effects” by the government without individualized suspicion or a particularized description of what data is being seized, much less probable cause. This protection was in response to colonial British misuse of writs of assistance, which authorized indiscriminate searches rather than targeted ones.

The justifications for exempting mass surveillance from constitutional protection vary. For Section 702, the government has taken the position that U.S. persons’ communications caught up in the dragnet, either due to overcollection or because they were communicating with someone outside the United States, do not require a warrant prior to initial collection or secondary access by the FBI and several other agencies. The argument is that if the initial collection was not aimed at Americans, the information is free from constitutional protection for any later uses, even for reasons far afield from the initial rationale for collection.

Other arguments rest on the claim that metadata is outside the Fourth Amendment, despite its demonstrated ability to reveal intimate details of all of our lives. Still others rest on the Supreme Court-created Third Party Doctrine, which holds that the Fourth Amendment does not apply to data shared with companies that provide us with services. Some turn on whether analysis by machine counts, claiming that only “human eyes” matter—a particularly troubling argument with the rise of artificial intelligence. What’s more, the government has used doctrines like standing to limit the ability of those subjected to mass surveillance to seek constitutional protection. No matter the argument, the goal is the same: to place the mechanisms and fruits of mass surveillance outside the protections of the Fourth Amendment.

The overarching truth is that, due to the concerted efforts by the government since 9/11, and the rise of technologies in recent years, the slice of Americans’ lives and data that are actually protected by the Fourth Amendment has shrunk significantly in the past 25 years. Together, with the technical capabilities of mass surveillance and the increased ability for that data to be analyzed using AI tools, the “security in our papers and effects” that the constitution promises seems increasingly illusory.

In addition to the Fourth Amendment, mass surveillance creates tensions with the First Amendment. The Constitution has long recognized that the right to freedom of speech requires a zone of privacy against governmental surveillance. The right to anonymous speech as well as the right of association both recognize the chilling effect that surveillance creates for people saying unpopular things or attempting to organize for political or other societal change. Mass surveillance grants the authorities the ability to track those people, both in real time and historically, that is inconsistent with actual techniques of freedom of speech and assembly.

That is why the recently released 2026 U.S. Counterterrorism Strategy is so troubling. On page seven, the White House expressly states that it intends to target domestic activists with its heretofore foreign-targeted powers. It says that the government “will prioritize the rapid identification and neutralization of violent secular political groups whose ideology is anti-American, radically pro-transgender and anarchist” and “will use all the tools constitutionally available to us to map them at home, identify their membership, map their ties to international organizations like Antifa.” While framed as targeting “violent” groups, it’s clear that the government intends to use its national security tools, presumably including the tools of mass surveillance, against Americans in ways that will create profound tensions with the First Amendment rights of people to organize and communicate privately.

Costs Due to Mistakes and Abuse

Even assuming some utility from mass surveillance—a fact we do not dispute, even if the public record is shaky and conclusory—the history of both the national security and domestic uses of mass surveillance confirms that these tools are inevitably misused, and that mistakes have impacted huge numbers of Americans. The past twenty-five years have demonstrated that it is not possible to surveil the entire US population while staying within the bounds of even a very generous legal framework like Section 702.

As Rep. Zoe Lofgren (D-Calif.) recently stated in discussion of Section 702 in an interview with Tech Policy Press: “backdoor searches have been used improperly for protestors, 19,000 campaign donors, members of Congress, journalists, government officials, a state court judge who had complained to the FBI about police misconduct. It has been abused substantially in the past.” The NSA experienced so much abuse of its mass surveillance tools by actual or aspiring romantic partners and ex-spouses that an internal name emerged for it: “LOVEINT,” or Love Intelligence.

That same pattern of abuse is now emerging at the domestic law enforcement level. A Texas police officer misused, and then lied about, using license plate readers to track a woman suspected of seeking an abortion. Multiple law enforcement officials have been accused of tracking people they either wished to have a relationship with or who were their exes. And mass surveillance technologies have been used to track both immigration targets and citizens engaging in their First Amendment-protected right to track and record the police.

Mistakes are inevitable with collections of data of this size and scope. The history of the FISA court’s reviews of Section 702 is littered with examples of the NSA not being able to follow its own rules limiting the scope of what it collects and analyzes, even after having been given multiple chances by the court. On the local level, the technical protections that Flock, for example, put in place have repeatedly been insufficient to stop “accidental” sharing its data with out-of-state law enforcement. These mistakes have fueled growing efforts by local communities across the country to remove license plate readers. Those efforts should be the first step in a broader reconsideration of mass surveillance.

More generally, ubiquitous surveillance carries a real societal cost. The chilling effects are real and pervasive, and they tend to fall hardest on the most marginalized members of society. Moreover, social progress requires the ability to experiment in secret. It’s hard to imagine a society progressing morally to the point of accepting and legalizing things like marijuana use or gay marriage if the earliest signs of that shift are snuffed out because of overzealous surveillance.

Reversing Course

While a cost-benefit analysis is not the best frame for deciding constitutional rights, it is a place to start to evaluate government policies. If the costs are too high and the benefits too small, what should the public do? While the policy and legal frameworks can be individually complex, mass surveillance is a problem in all of its applications. So too should solutions be comprehensive rather than piecemeal.

One comprehensive strategy is to reset the promise of the Fourth Amendment and recognize that a warrant is required prior to collection, access or use of information gathered through mass surveillance. This would apply to collections that include U.S. persons, whether done for national security or domestic purposes. This protection would apply regardless of whether the information is in the form of metadata. It would apply regardless of whether the information is held in homes or by services people rely on, such as telephones, internet or social network providers, or by private entities utilizing mass surveillance for their own purposes. By passing this legislation, Congress could ensure this rejection of mass surveillance, and include real enforcement such as a private right of action and an automatic exclusionary remedy in criminal prosecutions. The courts could also recognize this protection of “papers and effects” directly as a plain language interpretation of the Fourth Amendment.

There are already a number of efforts that take on pieces of mass surveillance. Section 702 has expired and should remain so. This was due largely to efforts to block the “back door” access to Section 702-collected data without warrants. The bipartisan “Fourth Amendment is Not for Sale Act” would prevent the government from purchasing data that it would otherwise need a warrant to obtain. The Supreme Court itself has already been chipping away at the Third Party Doctrine, with a recent step in the rejection of mass geofence warrants—warrants seeking the identities of individuals based upon their proximity to a crime—in Chatrie v. United States. Now, such warrants fall, at least initially, under the Fourth Amendment.

A more comprehensive approach would also address mass surveillance carried out by private companies, and to ensure that Americans have the right to encrypt and secure their data. There are many reasons the United States would benefit from a comprehensive privacy law—and curbing mass surveillance is one of them. Addressing mass surveillance is certainly one of them. Ideas such as the banning of secondary uses of data—with roots in the Fair Information Practice Principles from the 1970s—are worth pushing forward. So are moves such as creating fiduciary duties for mass data collectors. There are many more ways to curtail private companies’ mass surveillance while staying within constitutional boundaries. But addressing the costs of mass surveillance by both companies and governments is even more important in a world where AI agents are making decisions both about the public and on their behalf based on their data and observed behavior.

Twenty-five years after the U.S. government embraced mass surveillance, it’s time to evaluate it as a whole, and consider responses that address the problem as a whole. Americans must ask: Is it consistent with a self-governing democracy to have systems that watch everyone everywhere? Is the public comfortable with governments—federal, state, local—that seek to “know it all” about its citizens? Is the public comfortable with private mass surveillance in its own right and as it’s being increasingly used to fuel government surveillance? These questions have long needed serious consideration. But as it becomes increasingly evident that the Trump administration is using mass surveillance to keep itself in power, stifle dissent, and undermine political opponents, these questions are now more urgent than ever.

Wireless Routers as Motion Detectors

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/09/wireless-routers-as-motion-detectors.html

Comcast has added motion detection as a feature to its wireless routers:

The feature sends push notifications to users when motion is detected near a connected device, such as a TV or printer. It has different settings for when people are home, asleep, or away. The Xfinity app also lets users see live motion activity and a feed of recent activity.

Comcast acknowledges that the system has some limitations. Home size, layout, building materials, and the placement of the router and connected devices can all affect its ability to detect motion. Comcast says it does not guarantee its performance.

Sounds like a great surveillance tool. And also:

But the biggest privacy concern comes directly from Comcast’s own support page, which says information generated by WiFi Motion may be shared with third parties.

“Comcast may disclose information generated by your WiFi Motion to third parties without further notice to you in connection with any law enforcement investigation or proceeding, any dispute to which Comcast is a party, or pursuant to a court order or subpoena,” the page reads.

Spyware for Babies

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/spyware-for-babies.html

The New York Times has a long article (alt link) on surveillance systems aimed at babies. They are increasingly using AI.

Nanit and its rivals want to own 24/7 health tracking for the sub-four-foot set. And their already astonishing levels of baby data collection are just the beginning. Nanit recently raised $50 million from investors to expand its use of A.I. and use its camera to track speech and language development, motor skills and more, while extending its presence in children’s bedrooms into early adolescence.

Adversarial Clothing Designed to Fool Facial Recognition Systems

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/adversarial-clothing-designed-to-fool-facial-recognition-systems.html

There are many companies manufacturing adversarial clothing designed to confuse facial recognition systems.

It’s a cool idea, but I worry that it’s mostly security theater:

“Our patterns play with that chaos, confuse algorithms and make it way harder to pin you down,” he said.

Bell, however, said “none of these products are tried and tested, and a lot of these surveillance technologies can deal with a little resistance … [but] even if the designs don’t necessarily work perfectly, fashion is also a visible sign of resistance.

“This is consumers collectively coming together to make a visible statement.”

Without serious testing, there is no reason to trust the technology. And even with testing, there is no reason to trust that a new version of the facial recognition software doesn’t break the anti-surveillance properties.

I don’t want people to mistakenly rely on this stuff.

Facial Recognition at Madison Square Garden

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/facial-recognition-at-madison-square-garden.html

Last month, the story broke (alternate link) that Madison Square Garden uses facial recognition software on everyone entering the facility, and—among other groups—flags activists that oppose using facial recognition.

Turns out that the system was shut off for Taylor Swift’s wedding.

Evan Greer—one of the people that MSG alerts on—comments:

Ironically, Swift herself has reportedly used facial recognition at her own concerts to identify stalkers. This “privacy for me, surveillance for thee” attitude feels like a perfect encapsulation of the future we’re already living in: one where wealthy elites can afford privacy, while the rest of us are forced to live in a corporate surveillance panopticon.

Whatever privacy measures Swift had in place for the wedding seems to have worked. No photos have leaked online.

American Being Prosecuted for Wiping His Phone Before Handing It Over to Border Officials

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/american-being-prosecuted-for-wiping-his-phone-before-handing-it-over-to-border-officials.html

He’s being prosecuted for giving border officials a code that wiped his phone:

The case centers on a feature included in GrapheneOS, a custom Android operating system that runs in place of the software on most modern Google Pixel devices. Tunick’s attorneys confirmed GrapheneOS was running on his phone.

The software feature allows the device owner to set a passcode that deliberately wipes the contents of that device if entered instead of the user’s unlock passcode.

Tunick’s case also raises ongoing questions about what constitutional rights can be invoked at the border, which the U.S. government has long asserted is not U.S. soil until a person is authorized to enter.

Right. And he wasn’t under arrest, either.

Three more news stories.

Graphine says that the feature is “completely legal“:

GrapheneOS is completely legal. We have no obligation to weaken any of the security protections it provides. Creating and using GrapheneOS is strongly protected by the US constitution. Laws attempting to make it illegal or require weakening the security would be unconstitutional.

It’s hard to know how much the Constitution matters in the US right now.

Axon Is Another License Plate Surveillance Company

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/axon-is-another-license-plate-surveillance-company.html

Governments are switching, but I’m not sure it makes a difference:

…some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings.

[…]

Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use.

We’re open sourcing our privacy proxy CLI

Post Syndicated from Hannah Wang original https://blog.cloudflare.com/open-sourcing-our-privacy-proxy-cli/

Debugging privacy-preserving protocols is hard. Oblivious HTTP has several different steps across four different parties, not to mention binary HTTP encoding and details spread across many draft RFCs. We've taken what we've learned operating protocols like Oblivious HTTP at the scale of millions of requests per second, and wrapped it up in a nice, clean CLI tool — that we are open sourcing today.

We call it our privacy-client, or pvcli. We’re releasing it under the Apache-2.0 License, and it is open for contributions.

Here’s a single line of code that executes a full Oblivious HTTP request with a relay, gateway and origin. Don't worry if you don't know what that means, we'll cover it below.

We’ll explain why we built this tool, and show just how handy it can be.

Why privacy protocols can be hard to debug

Let’s take a closer look at our motivation for creating pvcli. Over time, the Privacy team’s product suite and customer base grew. We added products like Privacy Proxy and Privacy Gateway, which power Apple’s Private Relay, Microsoft’s Edge Secure Network VPN, Flo Health’s Anonymous Mode, and more. With it came an increasing amount of special customer requirements, domain knowledge, and complexity. As a result, we saw increased friction in development and incident response.

To see this in action, let’s look at how one of our products implements Oblivious HTTP, also known as OHTTP. First, a quick primer. OHTTP provides users with a privacy guarantee: no one can know both who made a request and what they’re requesting. To achieve this, OHTTP requires two servers, a relay and a gateway, operated by two non-colluding parties. 

Below is a sequence diagram of OHTTP, where our customer owns the relay and Cloudflare owns the gateway. At a high level, OHTTP can be broken down into these steps:

  1. Client gets public key from the gateway.
  2. Client encrypts the request and sends it to the relay.
  3. Relay removes “who” the client is from the encrypted request, and sends it to the gateway.
  4. Gateway decrypts the request, and sends it to the target.
  5. Target processes the request, and sends a response to the gateway.
  6. Gateway encrypts the response, and sends it to the relay.
  7. Relay sends the encrypted response to the client.
  8. Client decrypts, and gets the plaintext response.

It involves quite a bit of back-and-forth, as you can see: 

Each step is a potential point of failure that we have to consider while debugging!

In particular, we saw certain kinds of problems when debugging OHTTP.

  • Customers asked for ways to test the live system from their end, and we often wrote one-off, custom clients for our customers specific deployments.
  • Figuring out which step caused an issue was time-consuming. Was the root cause a bug in our system or our customer’s system?
  • Examining raw bits was tedious and highly prone to human error. OHTTP builds on binary HTTP, which is a binary encoded HTTP request. Anytime we needed to check the binary encoding, we were painstakingly going through raw bits.

As a result, we decided to place all of our privacy protocols in one tool. It has a clean interface that’s already familiar, displays every single step of the protocol in order, and is flexible enough to support new protocols and architectures.

To see the difference this makes, let’s see an OHTTP debugging scenario — before and after pvcli.

Debugging without pvcli

Say we operate an OHTTP relay that sits in front of a customer's gateway. The customer has asked us to do an end-to-end test with a request:

Recall the OHTTP steps from earlier. The first step is to fetch the public key from the gateway. We use curl to fetch it from the customer gateway and get this back:

That’s a big binary string in hex. To make sense of it, we look at OHTTP RFC 9458 §3 and parse it manually:

  • 0029 is 41 in decimal, telling us this public key entry has 41 bytes associated with it.
  • 55 is the public key ID.
  • 0020 identifies the asymmetric encryption method we can use. In this case, DHKEM(X25519, HKDF-SHA256).
  • b9bb667e2230dc01c6d6cc047f94a1083beb185c63e50ec09f7692a5a0832540 is the public key.
  • 0004 tells us there are 4 bytes of symmetric cryptographic IDs that follow.
  • 0001 and 0001 identify the symmetric encryption methods we can use: HKDF-SHA256 and AES-128-GCM.

We repeat this process for however many public keys are in the binary string.

Next, we convert our original HTTP request into binary HTTP, referencing RFC 9292. We manually craft the binary with the help of some bespoke scripts:

We verify each field:

  • 02 means it's an indeterminate-length request
  • 04504f5354 is POST
  • 056874747073 is https
  • 117461726765742e6f687474702e696e666f is target.ohttp.info
  • and so on

Finally, we form a wrapper HTTP request that will hold our OHTTP request. To do so, we spend some more time writing another makeshift script that encrypts the binary HTTP request in the manner OHTTP specifies, using the public key from earlier. We create a header, which is the concatenation of public key ID, asymmetric encryption method ID, and symmetric encryption method IDs. Then, we concatenate header and encrypted binary HTTP request, resulting in:

We put those bytes into the body of our wrapper HTTP request, and send it to our relay. We get back a response.

What does that mean? We reach out to the customer to ask if they can share logs from their gateway. In the meantime, we double-check the bits we've crafted. The decoded public keys look fine. The binary HTTP request… Oh! We see:

BHTTP is length prefixed. That means we specify a length (0x0a is 10 in decimal), and then 10 bytes follow. But here, 11 bytes follow. There is an extra 20 before the 00. 20 represents a space character, so we must have accidentally added that when building the body. We remove the extra character, resend, and it works!

Debugging with pvcli

With pvcli, all of that is now a single command:

It handles all the binary parsing and encrypting for us, and prints logs in case we want to dive deeper:

What used to be a fragile process — involving manipulating bits, gluing together scripts, and referencing long RFCs — is now one command.

What pvcli can do

To install:

pvcli takes a lot of inspiration from curl. We designed it with the “principle of least surprise” in mind. As a result, a lot of the arguments are the same as curl’s! Try a quick GET request to our cdn-cgi endpoint:

If you’re curious about what is happening under the hood, you can use -v to get detailed logs:

Now, about that OHTTP command from earlier: you use –ohttp to tell pvcli to construct an OHTTP request. You pass in the relay as the –first-hop and the gateway as the –proxy. The target will be an echo server, so you can see what the target would see. In this command, we filled in the arguments with a relay, gateway, and target from ohttp.info.

Try running the command yourself!

We’ve encountered many cases where we wanted to pass headers to the relay, rather than the target. You are able to do that with --first-hop-header:

Similarly, we’ve also had cases where we wanted to authenticate to the relay with mTLS, to ensure that the correct client is talking with the correct relay. To do that, you can use –first-hop-client and --first-hop-key.

And it just works. Need to test a full Oblivious HTTP request with a relay, a gateway, arbitrary headers, and mTLS? Or perhaps only request through a gateway? Or maybe you just want to see the OHTTP key configuration? pvcli can do it with a single command, debugging included.

Why build our own tool?

There are some great tools for OHTTP that already exist. Martin Thomson’s Rust implementation and Chris Wood’s Go implementation were incredibly helpful when we built out our original OHTTP implementation a few years ago. But pvcli is not only focused on OHTTP. We’re looking to add as many privacy-preserving protocols as we can to the tool. So while there are other OSS tools out there for debugging OHTTP, nothing combines OHTTP, CONNECT proxying, MASQUE and Privacy Pass (coming soon) all in one place.

Contribute to pvcli

Oblivious HTTP is an amazing protocol, and we would love to see you use it. We hope that this tool helps people debug OHTTP and write their own OHTTP implementations. 

We are accepting contributions! To get started, clone the repo at https://github.com/cloudflareresearch/pvcli, and submit a pull request. 

If you're looking for ways to contribute, here are some things on our to-do list. For MASQUE, we plan to add support for proxying TCP over HTTP/3, and UDP and/or IP over HTTP/2 and HTTP/3. For OHTTP, we plan to support post-quantum cryptography, add timing/latency information, support Chunked OHTTP, and improve logging.

Contact us if you are interested in using Cloudflare’s OHTTP Relays and Gateways.

Cognyte Sells a Mobile Cell Surveillance Van

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/cognyte-sells-a-mobile-cell-surveillance-van.html

Yet another Israeli mass surveillance company:

Made by Israeli surveillance company Cognyte, the tech simulates a mobile phone tower, which forces nearby phones to connect to it. That enables cops to keep tabs on any phones in the vicinity ­ whether they’re owned by a suspect in a case or not. Cognyte’s contract with the state of Texas reveals that the simulator, called FalcoNet, can be concealed within the vehicles, hidden in a backpack for on-foot missions or attached to a helicopter. It’s the same technology as the infamous Stingray, one of the original cell-site simulators made by defense giant L3Harris.

MIT to Become Hotbed of AI Video Surveillance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/mit-to-become-hotbed-of-ai-video-surveillance.html

It’s a lot:

According to information obtained by The Tech, MIT is spending over $3 million on more than 500 AI surveillance cameras in academic buildings, residence halls, and outdoor areas along Memorial Drive. Installation of the new cameras, along with the wiring and infrastructure that will support them, began November 2025 and will likely continue until September 2026.

Technical specifications for the cameras suggest that they will be capable of collecting real-time face and object classification data, including detection of motion, loitering, crowds, face masks, and camera tampering. Individuals can also be automatically classified on the basis of clothing color, gender, and age, up to a distance of 35 feet (11 meters) from the camera. According to a statement from MIT spokesperson Kimberly Allen, any collected data is “retained up to 30 days,” unless an exception is granted.

[…]

Most of the new cameras, which are part of Hanwha’s Wisenet AI line, are marketed for their ability to identify and classify multiple objects with deep learning algorithms. They support resolutions ranging from 2MP to 4K while also recognizing faces, license plates, vehicles, and other objects in real time.

Nearly all cameras will accommodate a wide range of pan, tilt, rotate, and zoom motion and will be monitored continually with Ai-RGUS, an AI camera software.

Yikes.

Protecting Privacy in an AI Era

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/protecting-privacy-in-an-ai-era.html

Daniel Solove argues in the Wall Street Journal (alternate link) that giving people control of their personal data is not an effective way to regulate privacy in this era. Instead, we need to hold companies accountable for their actions, similar to what we do with food and drug companies. Measures such as rigorous data minimization, fiduciary duties, liability for negligent or reckless technological design, liability for algorithms that cause harm, and multi-stakeholder review of technologies will be far more effective.

Paper.

AI Surveillance and Social Progress

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/ai-surveillance-and-social-progress.html

In the near future, AI-powered surveillance systems will be able to track everything we do in public, and much of what we do in private. And if we do something wrong—shoplift, litter, jaywalk, you name it—the system will notice, retain it, tie it to your official government record, communicate that fact to you, and provide real-time alerts to any relevant authorities… and maybe also to the general public.

Think of these systems as automated speed cameras, but on steroids. Only they’ll enforce not just speed limits, but any other rule you can imagine. And you won’t receive a ticket weeks later by mail; you’ll be informed about and fined for your violation immediately.

These systems will combine powerful AI, public and private surveillance via real-time facial recognition technology and digital tracking, mass databases and highly personalized enforcement. If deployed at scale, they will have profound chilling effects not just on personal freedoms, but democracy and social progress itself.

China has been developing its surveillance infrastructure for years. The country has over 600 million surveillance cameras, increasingly powered by AI and facial recognition to enforce legal and social rules. Take the case of Lao Duan, a Chinese citizen blacklisted by the system after he lost his job and was unable to repay a series of loans. When he visited Beijing, the city’s AI surveillance system identified him by his face at a major intersection and displayed his face, name and citizen ID number on a large electronic billboard nearby with a message that he was an untrustworthy person. Similar systems are now being deployed across China and integrated with its infamous online monitoring, censorship and social credit systems.

AI surveillance is now being experimented with in North America, South America, Europe, Asia and Africa. According to a new report, the US Department of Homeland Security is rapidly increasing its use of AI-based surveillance, including facial recognition and the monitoring of social media accounts, to keep tabs on immigrants, dissidents, journalists, legal observers and protesters. While the systems are ostensibly used to maintain security and public safety, the real aim is often social control. Larry Ellison, CEO of Oracle—a powerful tech giant that works closely with the Trump administration—has said: “Citizens will be on their best behavior because we’re constantly recording and reporting.” The chilling effects are the point.

AI surveillance raises a range of public policy challenges: technical biases, unauditable systems, and inflexible automated law and social rule enforcement that can promote discrimination and undermine transparency, accountability and the rule of law. But we believe the most urgent and long-term impact will be its broader chilling effects.

In a new book, Chilling Effects: Repression, Conformity, and Power in the Digital Age, Jon Penney explains how surveillance, technology and power can be weaponized to influence behavior at scale. Surveillance, personalization, uncertainty and authority are all key mechanisms to increase the scale and impact of chilling effects. They cause people to self-censor their words and actions, to become more conformist and compliant and thus easier to manage and control. And the effects are additive: the more mechanisms employed, and the more powerful the form, the greater the chill.

Computerization has long allowed data collectors to track our locations, collect lists of whom we communicate with, and monitor our spending habits—unless we use cash. What’s new is an unprecedented fusion of each of these mechanisms, persistent and unrelenting. AI brings an analytical ability to spy on the contents of our communications, and to answer sophisticated questions about our whereabouts and activities: actions that previously required human analysts are now automated. The result will be a kind of supercharged societal level of chilling effects where fear, self-censorship and groupthink reign, and dissent, creativity and innovation become increasingly rare.

In this atmosphere of fear and conformity, risky ideas, social activism and self-reinvention—especially by disfavored groups and targeted populations—are also chilled. This will have long-term effects on social progress.

Consider the relatively recent societal normalization of same-sex relationships and the recreational use of marijuana. Over the decades, those ideas slowly progressed from being both immoral and illegal, to moral but still illegal, and finally to both moral and legal. But in order for any of that to happen, there had to be a counterculture that was able to experiment and eventually demonstrate to the world that morality could change over time. To the extent that AI surveillance chills this sort of experimentation in public or in private, social progress becomes impossible.

There are no real historical precursors to this; these technologies are too new. Even the most notorious and large-scale domestic surveillance program in US history, the FBI’s use of wiretapping, physical mail opening, informants and paper index cards to track alleged communists during the 1950s and 1960s, appears genuinely archaic in light of modern AI-enhanced surveillance. So does East Germany’s human-centric surveillance network during the cold war. Only science fiction, from the likes of George Orwell or Aldous Huxley, comes close. But even Big Brother’s “telescreen” feels decidedly mid-20th-century by comparison.

But we need not sit idly. Now that we recognize the danger of AI-enhanced mass surveillance, we can make the policy choices not to implement it. Bans on facial recognition and other forms of identification tech can slow development; robust new privacy and data protections can restrict data tracking and retention; AI regulations can curtail its most invasive uses; and structural reforms can help us scrutinize and break up powerful state/tech cartels that pave the way for technological excesses like AI surveillance.

The chill of AI-powered mass surveillance will suffocate the very foundations of healthy democratic societies. But we can still choose a different path.

This essay was written with Jon Penney, and originally appeared in The Guardian.

Flock Cameras Can Surveil Cars Without License Plates

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/flock-cameras-can-surveil-cars-without-license-plates.html

This is from a 2024 company presentation:

Officers can also tap into data showing a car’s decals, bumper stickers, back and top racks—along with temporary and unique state tags.

Flock calls it a “Vehicle Fingerprint” and it’s touted as a way for law enforcement officials to get more information “even when you don’t have full plate information,” the company’s presentation shows.

The company gives police officers the ability to search that data as well, to “build stronger cases with less information upfront.” That includes being able to locate multiple vehicles law enforcement officials believe are moving together and what Flock calls a “multi geo search.”

This kind of thing is older than AI; I wrote about it in my 2014 book Beyond Fear. Edward Snowden revealed that the NSA was using cell phone location data to track phones that were habitually near each other.

As bad as Flock is, remember that anyone with broad access to cell phone location data can do the same thing.

Papa Johns Surveillance-Based Advertising

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/07/papa-johns-surveillance-based-advertising.html

Papa Johns is spying on people’s buying activities to predict when they are low on food:

The pizza chain recently tapped NBCUniversal, Instacart and the dentsu-owned media agency Carat for help reaching consumers when they’re low on groceries—and thus more likely to be swayed by a mouth-watering ad. The idea is to reach hungry consumers by “knowing what is in their fridge without being too creepy,” said Carrie Drinkwater, chief investment officer at Carat.

To achieve that goal, NBCU and Instacart created a custom audience of shoppers who regularly purchase grocery staples on Instacart, such as eggs, milk, meat and produce. Based on that data, Papa Johns can determine which days of the week certain consumers are likely to run out of groceries and serve them an ad on NBCU streaming content accordingly. The brand served custom creatives to consumers based on their food preferences—such as whether they buy meat regularly—with QR codes and calls to action such as, “Light on groceries?” or “Empty fridge?”

Back in 2012, we learned (from Target and its campaign that detects when someone is pregnant) that the trick is to hide the knowledge in other, wrong, information. So the way for Papa John’s to not be “too creepy” is to deliberately get it wrong sometimes.

But still, ugh.

The Realities of AI Video Surveillance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/06/the-realities-of-ai-video-surveillance.html

The Financial Times has a good article on how AI is changing the capabilities of video surveillance, with information from both Israel/Iran and Russia.

I wrote about this sort of thing a few years ago, how AI enables mass spying in the way that computers and networks enabled mass surveillance. The interesting development in the article is that AI allows people to ask natural language questions about video footage to AIs—and AIs can answer them.

In contrast with older tools restricted to a few dozen preset searches, these new tools allow an almost unlimited range of enquiries by enabling language-based searches on video.

That lets intelligence officers hunt through massive streams of videos using simple search terms, such as two men handing a bag to each other; a person who has changed their appearance, or has changed clothes multiple times in a day; or a vehicle that has recently been painted over, or has driven past the same spot several times in a short period.

“This is the holy grail of surveillance,” said a European official whose country uses the technology on its cities. “We are able to look for behaviour, not objects ­ it has created a world of new possibilities.”

Meta Is Testing Facial Recognition for Police and Military

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/06/meta-is-testing-facial-recognition-for-police-and-military.html

We know that ICE wants to deploy eyeglasses with facial recognition that can identify people in real time.

Turns out Meta is prototyping the feature with a Pentagon supplier. (Alternate news story.)

Professional Athletes and Wearables

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/06/professional-athletes-and-wearables.html

I haven’t thought about the privacy issues surrounding professional athletes and wearables.

Wearables present serious privacy issues for “Average Joe” consumers, who are entrusting tech companies to safely store and protect their biometric data. Imagine the stakes for a professional athlete, whose entire livelihood could be affected by a single biometric data point. To give one of many realistic hypotheticals: a basketball player has a terrible game, and the coach wonders if they showed up to the gym hungover. The coach has access to the player’s wearable data, and checks to see when they went to sleep, as well as what their heart rate looked like during the night. Should the player have been out partying before a game? No. Should the coach be able to surveil them? Definitely not.

It will not surprise you to learn that there’s an emergent gambling angle here: sports leagues would love to commercialize players’ biometric data, and sharp bettors would love access to data about, say, a hungover player. “We’re going to get to a spot where people are betting not just on the velocity of the puck that was shot by a player in the NHL playoffs, but on what the heart rate of a certain player is going to be running down the field,” said Helen “Nellie” Drew, the director of the University of Buffalo’s Center for the Advancement of Sport, and a professor of practice in sports law.

There are other practical considerations, too. What if wearable data reveals that a player isn’t as speedy as they were before, and a team uses that data against the player during contract negotiations? What if a wearable reveals a player is favoring their leg, or is at greater risk of injury? This information is potentially beneficial to a training staff and an athlete, so long as it’s disclosed and used in a responsible manner—­a critical, mostly unresolved caveat. “Aging and injured players are the most at-risk” of wearable data being used against them, said Michael LeRoy, who researches sports labor laws and AI, and is a professor at the University of Illinois’s School of Labor and Employment Relations.

The bit about gamblers is particularly scary.

I have often said that surveillance tech is generally deployed first against people with diminished rights: children, prisoners, military personnel, the mentally impaired. This is another early use case with different dynamics. The surveilled are wealthy and powerful, and—in many cases—unionized.

The FCC Wants to Eliminate Burner Phones

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/06/the-fcc-wants-to-eliminate-burner-phones.html

A proposed FCC rule would kill burner phones: phones whose accounts are not attached to a particular person.

The FCC plans to do this by legally forcing the country’s telecoms to store a wealth of personal information about essentially all phone customers, including a government issued identification number and their physical address, alarming privacy advocates and civil rights activists who compare the measures to those from authoritarian countries where it can be difficult to buy a mobile phone plan without giving up your identity.

The proposed change would drastically shake up how people obtain phone plans in the U.S., and have all sorts of privacy and cybersecurity knock-on effects. The FCC is proposing the data collection partly as a way to combat scammers, with telecoms being required to collect other information on business and foreign customers like the intended use case of their bulk phone plan purchase and their IP address. But the changes would mean telecoms collect data on all new and renewing customers, and the FCC provides a long list of other things that the collected data could help authorities with.

Alternate link.